Seminar 12: (free discussion on nowadays popular topics in bioinformatics and computational biology)

Date: 1 marec 2016Speaker: Kristina Ban, FIŠ. ​​​Abstract: This seminar is intended not as a usual lecture, but rather as a free discussion. Kristina Ban will open and moderate the discussion on nowadays popular topics in bioinformatics and computational biology, mainly referring to biological networks (protein-protein interaction networks, metabolic networks, phylogenetic trees, gene expression networks), including different computational methods used in this vibrant field, such as network alignment. This will be the first part of a series ofseminars/discussions on this topic.

Seminar 11: Analiza družbenih omrežij: sedanjost in prihodnost

Date: 3 februar 2016Speaker:​​​Robert Kopal, Visoka šola Algebra, Zagreb​, HrvaškaAbstract:​ Social network is formally defined as a group of interconnected "individuals". These individuals are connected by one or more types of relations whose patterns occupy the attention of scientists and researchers. They relate to each other by either some kind of cooperation, or competition/conflict, and are referred to as entities or actors. Their relations can be depicted by a graph in which each entity (actor or individual) is depicted as what is in graph theory called a node, and their relations are depicted as links (ties). Social Network Analysis (SNA) is the structured analysis of social networks. Using mathematical tools and computer software it helps measure different types of variables related to the distance between the nodes (vertices) and the types of their associations, or links (edges), in order to determine the degree and type of influence one vertex has on another. SNA helps identify hidden associations and degrees of influence between the dots. The practical SNA application will be demonstrated in the following areas: telco industry, banking, national security, HR.

Seminar 10: Globoko vzpodbujeno (strojno) učenje v robotiki

Datum: 13 januar 2016Predavatelja: Matjaž Omladič in Blaž Sobočan, Inštitut Jožef Stefan in Fakulteta za matematiko in fiziko Univerze v Ljubljani Abstract: The lecture will present the work in progress on robot control by joining efforts of existent group in the robotics department at IJS with the newly joined mathematicians. We concentrate on applying neural networks to the usual methods of reinforcement learning trying to get a better result. Most of applications of neural networks so far are limited to vision problems and various tries to use it in controlling robots did not turn our to be particularly successful. A new approach in neural networks, given also a new name - deep learning (possibly to forget about the less successful applications of the method), may deserve another try since a number of successful applications has been reported in various fields. This Summer we started by applying one of the deep learning methods developed as an optimization method. We developed a combination of these techniques with reinforcement learning by viewing it as an optimization problem. Later we found an even better method of deep learning for our purposes, namely the method of autoencoders. These were first developed for reproduction of images and aimed at reducing the number of starting nodes (visible units - possibly representing certain features) to a much smaller number of nodes (hidden features) but than going back to the starting number of nodes in order to reproduce the starting image with a smaller number of hidden features. This method was applied to a simulator and gives interesting results. We believe it deserves to be tested via a more realistic experiment and may lead to a publishable paper. The leading investigator is dr. Matjaž Omladič, supported by dr. Bojan Nemec and dr. Aleš Ude. Some work was done by a master student at Faculty of Mathematics, Physics and Mechanics, Blaž Sobočan, who will also present some of the tests using the Python libraries.

Seminar 8: The construction of an annotated corpora in Slovenian language and the evaluation performance of sentiment based classification techniques

Date: 15 December 2015Speaker:Jože Bučar, Faculty of Information StudiesAbstract: The Web today is a growing universe of websites and a huge repository of structured and unstructured data. Enticing as it is with varied and freely accessible data, it is a remarkable source of data for every data scientist. Especially when dealing with textual data, language scientists are more frequently turning to the Web as a source of language data, precisely because it is so enormous, because it contains facts, emotions and opinions, which we can extract, or simply because it is free and instantly available. In this talk we introduce the methodology and tools that were required for their construction. Web crawlers retrieve the content of web pages in Hyper Text Marup Language (HTML) format from the news archives. The annotation process was carried out on three levels independently, i.e. document-level, paragraph-level, and sentence level. The annotated corpora contain more than 10,000 documents. Afterwards we provide some examples of its use, we evaluate performance of sentiment based classification techniques, and finally, we estimate the proportion of negative, neutral, and positive news in web media.

Date: 8 December 2015Speaker:Ljupco Todorovski, Faculty of government, Univeristy of LjubljanaAbstract: Modeling dynamical systems from empirical data is a genuine inverse problem: Given observations of the system dynamics, one looks for a model the most appropriate structure and parameters that explain these observations. The problem naturally fits the framework of supervised machine learning that aims at training a model from data. However, supervised methods are often prone overfitting - in attempt to capture all the information from the data, the model becomes so detailed that it ends up having poor predictive performance on new data unseen in the training process. I will present our method of dynamical systems modeling, referred to as process-based modeling, emphasizing our approach to overfitting avoidance. In this context, I will also present empirical results evaluating different approaches to overfitting avoidance, ranging from classical minimal-description length principle that introduces general bias towards simpler models, through reducing the variance by using ensembles of models, to strengthening the bias by using domain-specific knowledge. The last approach relies on adding expected domain-specific properties of the model dynamics to the objective function that measures the fit of the model behavior to the training data. The ability of the method to take into account such properties of the model behavior have a very nice and somehow surprising consequence: the presented method can also address the new and practically relevant task of designing systems that produce behaviors with given desired properties. The presented work is a result of a collaborative research with Sašo Dzeroski, Nikola Simidjievski and Jovan Tanevski from Jožef Stefan Institute, Will Bridewell from Naval Research Laboratory and Pat Langley from University of Auckland.

Date:7 October 2015Speaker: Pavle Boškoski, Jožef Stefan Institute, LjubljanaAbstract:Despite the maturity of fuel cell development, the supporting systems that influence their optimal usage, reliability and longevity still require substantial development. Such supporting systems require a set of algorithms and hardware subsystems for control, condition monitoring (CM) and power conditioning. Achieving such a system four criteria should be met: (i) such a system should be capable of performing sufficiently fast and accurate estimation of the fuel cell CM, (ii) it should allow straightforward integration without any prior characterization of the fuel cell system, (iii)its output should be describe the overall health of the fuel cell preferably in the interval [0,1] and (iv) the algorithms should be computationally efficient allowing embedded implementation. Various faults alter the PEM fuel cell impedance characteristic. Therefore, electrochemical impedance spectroscopy is employed for the purpose of CM. Currently, the most popular technique for estimating PEM fuel cell impedance employs sinusoidal waveforms. Notwithstanding the precise impedance measurements, such an approach suffers from long measurement period. Significantly, faster impedance estimation can be achieved by employing pseudo-random binary sequence (PRBS) as a perturbation signal. The impedance characteristic is computed using continuous wavelet transform with Morlet mother wavelet. With such an approach, EIS is performed in the frequency band from 0.1 Hz to 500 Hz within 60 seconds. By using PRBS excitation signal the impedance components among different frequencies become dependent complex random variables. Determining their distributions allows optimal selection of the decision thresholds of the CM system. As a result, the CM thresholds are calculated employing solely data acquired from the system in reference state of health and the desired false alarm rate. The overall fuel cell condition is estimated by fusing the information contained in particular impedance component through their joint cumulative distribution function based on copula functions. The output of the joint CDF can be directly used as an overall scale-free condition indicator. The proposed algorithms allow computationally efficient implementation. As a result, using these algorithms a complete solution for condition monitoring system based on modular DC-DC converter, 90 channel fuel cell voltage monitor and an embedded diagnostic algorithm. The complete solution has been evaluated on a 8.5 kW fuel cell power system.

Seminar 2: The influence of knowledge on processes within loosely coupled systems in the field of public safety

Date:17 June 2015Speaker:Jernej Agrež, Faculty of information studies in Novo mestoAbstract: Between 2010 and 2014, the communities located in the Lower Sava valley experienced four flood events. The flood events occurred due to continuous rain in central, northeastern and southeast parts of Slovenia. The least threatening of the flood events included increased water level of the Sava and the Krka rivers, which isolated only a few houses from the rest of the community. The most devastating event caused several roadblocks, flooding the entire areas of the communities located close to the two rivers.To be able to understand the community learning in the flood threatened areas and to get insight, how emerged community knowledge influences flood response processes, we gathered meteorological and hydrological data from the Slovenian Environment Agency and data on the severity of the flood events from the Administration for Civil Protection and Disaster Relief database. We also conducted several semi-structured interviews with the residents in flood-endangered areas and extracted relevant information out of national defense documents, regional and local emergency standard operating procedures.Through the data analysis, we addressed following research questions: whether is it possible to define and assess processes in loosely coupled systems; whether is it possible to map and assess community knowledge; how community knowledge influences processes in the loosely coupled system; and finally, how knowledge based process pattern recognition could be used for ensuring public safety.

Seminar 1: Graph recoloring

Date:9 June 2015Speaker:Marthe Bonamy, Univesity of MontpellierAbstract: A proper k-coloring of a graph is an assignment of one color to each vertex such that no two adjacent vertices have the same color, and at most k different colors are involved on the whole graph. Given two proper k-colorings of a graph G, is there a way to recolor G from one coloring to the other while recoloring one vertex at a time and ensuring that G is always properly k-colored? In how many steps? We will present various conditions on the pair (G,k) for this to be possible in few steps. We consider in particular graphs with no long induced path and graphs with no long induced cycle. This is based on joint work with Nicolas Bousquet (McGill University).